numpy.prod

numpy.prod(a, axis=None, dtype=None, out=None, keepdims=False)

Return the product of array elements over a given axis.

Parameters:

a : array_like

Input data.

axis : None or int or tuple of ints, optional

Axis or axes along which a product is performed. The default (axis = None) is perform a product over all the dimensions of the input array. axis may be negative, in which case it counts from the last to the first axis.

New in version 1.7.0.

If this is a tuple of ints, a product is performed on multiple axes, instead of a single axis or all the axes as before.

dtype : data-type, optional

The data-type of the returned array, as well as of the accumulator in which the elements are multiplied. By default, if a is of integer type, dtype is the default platform integer. (Note: if the type of a is unsigned, then so is dtype.) Otherwise, the dtype is the same as that of a.

out : ndarray, optional

Alternative output array in which to place the result. It must have the same shape as the expected output, but the type of the output values will be cast if necessary.

keepdims : bool, optional

If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original arr.

Returns:

product_along_axis : ndarray, see dtype parameter above.

An array shaped as a but with the specified axis removed. Returns a reference to out if specified.

See also

ndarray.prod

equivalent method

numpy.doc.ufuncs

Section “Output arguments”

Notes

Arithmetic is modular when using integer types, and no error is raised on overflow. That means that, on a 32-bit platform:

>>> x = np.array([536870910, 536870910, 536870910, 536870910])
>>> np.prod(x) #random
16

Examples

By default, calculate the product of all elements:

>>> np.prod([1.,2.])
2.0

Even when the input array is two-dimensional:

>>> np.prod([[1.,2.],[3.,4.]])
24.0

But we can also specify the axis over which to multiply:

>>> np.prod([[1.,2.],[3.,4.]], axis=1)
array([  2.,  12.])

If the type of x is unsigned, then the output type is the unsigned platform integer:

>>> x = np.array([1, 2, 3], dtype=np.uint8)
>>> np.prod(x).dtype == np.uint
True

If x is of a signed integer type, then the output type is the default platform integer:

>>> x = np.array([1, 2, 3], dtype=np.int8)
>>> np.prod(x).dtype == np.int
True